I work on omics and digital health related projects at Stanford


All Publications

  • Feasibility and Validation Testing of the Walk.Talk.Track. (WTT) App for Remote Monitoring of the Six Minute Walk Test (6MWT) in Pulmonary Arterial Hypertension (PAH) Glinskii, V., Anderson, R., Lopez, A., Lin, A., Del Rosario, P., Hershman, S., Hess, J., Perez, V., Zamanian, R. EUROPEAN RESPIRATORY SOC JOURNALS LTD. 2021
  • Development and testing of Walk.Talk.Track. app - a wearables-based platform for mobile 6MWT monitoring Anderson, R., Glinskii, V., Lopez, A., Lin, A., Del Rosario, P., Hershman, S., Hess, J., Perez, V., Zamanian, R. EUROPEAN RESPIRATORY SOC JOURNALS LTD. 2021
  • Physical Activity and Its Association with Traditional Outcome Measures in Pulmonary Arterial Hypertension. Annals of the American Thoracic Society Minhas, J., Shou, H., Hershman, S., Zamanian, R., Ventetuolo, C. E., Bull, T., Hemnes, A., Chakinala, M. M., Mathai, S., Al-Naamani, N., Ellenberg, S., Matura, L. A., Kawut, S. M., Shcherbina, A. 2021


    Rationale Limitation of physical activity is a common presenting complaint for patients with pulmonary arterial hypertension (PAH). Physical activity is thought to be determined by cardiopulmonary function, yet there are limited data that investigate this relationship. Objective We aimed to study the relationship between right ventricular function and daily activity and its impact on health-related quality of life (HRQoL) in PAH. Methods Baseline data for 55 patients enrolled in PHANTOM, an ongoing multicenter randomized controlled trial of anastrozole in PAH were used. Post-menopausal women and men were eligible and underwent six-minute walk testing, echocardiography and completed HRQoL questionnaires. Each patient wore an accelerometer for 7-days. Multivariable linear regression models were used to study the association between tricuspid annular plane systolic excursion (TAPSE) and vector magnitude counts, and between daily activity and HRQoL. Principal component analysis and K-means clustering were used to identify activity-based phenotypes. K-nearest neighbors' classification was applied to an independent cross-sectional cohort from the University of Pennsylvania. Results The mean age of patients in PHANTOM was 61 years. 67% were women with idiopathic PAH as the most common etiology. A 0.4 cm increase in TAPSE was associated with an increase in daily vector magnitude counts (SS:34000, 95%CI:900-67000, p=0.004) after adjustment for age, sex, body mass index, etiology of PAH and wear time. A 1-standard deviation increase in vector magnitude counts was associated with higher six-minute walk distance (SS: 56.1 meters, 95%CI:28.6-83.7, p<0.001) and lower emPHasis-10 scores (SS:-3.3, 95%CI:0.3-6.4, p=0.03). Three activity phenotypes, low, medium, and high, were identified. The most active phenotype had greater six-minute walk distances (p=0.001) and lower emPHasis-10 scores (p=0.009) after adjustment for age, sex, body mass index, WHO functional class and parenteral prostacyclin use. Phenotypes of physical activity were reproduced in the second cohort and were independently associated with six-minute walk distance. Conclusion Better right ventricular systolic function was associated with increased levels of activity in PAH. Increased daily activity was associated with greater six-minute walk distance and better HRQoL. Distinct activity-based phenotypes may be helpful in risk stratification of PAH patients or provide novel endpoints for clinical trials.

    View details for DOI 10.1513/AnnalsATS.202105-560OC

    View details for PubMedID 34473938

  • Mapping the human genetic architecture of COVID-19. Nature COVID-19 Host Genetics Initiative 2021


    The genetic makeup of an individual contributes to susceptibility and response to viral infection. While environmental, clinical and social factors play a role in exposure to SARS-CoV-2 and COVID-19 disease severity1,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. We describe the results of three genome-wide association meta-analyses comprised of up to 49,562 COVID-19 patients from 46 studies across 19 countries. We reported 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3-7. They also represent potentially actionable mechanisms in response to infection. Mendelian Randomization analyses support a causal role for smoking and body mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19, with unprecedented speed, was made possible by the community of human genetic researchers coming together to prioritize sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.

    View details for DOI 10.1038/s41586-021-03767-x

    View details for PubMedID 34237774

  • REmote moBile Outpatient mOnitoring in Transplant (Reboot) 2.0: a randomized control trial protocol. JMIR research protocols Murray, K. R., Foroutan, F., Amadio, J., Posada, J. D., Kozuszko, S., Duhamel, J., Tsang, K., Farkouh, M. E., McDonald, M., Billia, F., Barber, E., Hershman, S. G., Bhat, M., Tinckam, K. J., Ross, H. J., McIntosh, C., Moayedi, Y. 2021


    BACKGROUND: The number of solid organ transplants (SOT) in Canada has increased 33% over the past decade. Hospital readmissions are common within the first year after transplant and are linked to increased morbidity and mortality. Nearly half of these admissions to hospital appear to be preventable. Mobile health (mHealth) technologies hold promise to reduce admission to hospital and improve patient outcomes as they allow real-time monitoring and timely clinical intervention.OBJECTIVE: To determine whether an innovative mHealth intervention can reduce hospital readmission and unscheduled visits to the emergency department (ED) or transplant clinic. Our second objective is to assess the use of clinical and continuous ambulatory physiologic data to develop machine learning algorithms to predict risk of infection, organ rejection, and early mortality in adult heart, kidney, and liver transplant recipients.METHODS: REmote moBile Outpatient mOnitoring in Transplant (Reboot) 2.0 is a two-phased single-center study to be conducted at the University Health Network (UHN) in Toronto, Canada. Phase 1 will consist of a 1-year concealed randomized control trial of 400 adult heart, kidney, and liver transplant recipients. Participants will be randomized to receive either personalized communication using a mHealth application in addition to standard of care phone communication (intervention group), or standard of care communication only (control group). In phase two, the prior collected dataset will be utilized to develop machine learning (ML) algorithms to identify early markers of rejection, infection, and graft dysfunction post-transplantation. The primary outcome will be a composite of any unscheduled hospital admission, visits to the ED or transplant clinic following discharge from the index admission. Secondary outcomes will include: 1) patient-reported outcomes using validated self-administered questionnaires; 2) 1-year graft survival rate; 3) 1-year patient survival rate; and 4) number of standard of care phone voice messages.RESULTS: At the time of this manuscript's completion, no results are available.CONCLUSIONS: Building from previous work, this project will aim to leverage an innovative mHealth application to improve outcomes and reduce hospital readmission in adult SOT recipients. Additionally, the development of ML algorithms to better predict adverse health outcomes will allow for personalized medicine to tailor clinician-patient interactions, and mitigate the healthcare burden of a growing patient population.CLINICALTRIAL: NCT04721288;

    View details for DOI 10.2196/26816

    View details for PubMedID 34528885

  • Combining digital data and artificial intelligence for cardiovascular health. Cardiovascular research Soto, J. T., Hershman, S. G., Ashley, E. A. 2021; 117 (9): e116-e117

    View details for DOI 10.1093/cvr/cvab211

    View details for PubMedID 34320165

  • (ReBOOT) REmote moBile Outpatient MOnitoring in heart Transplant: A pilot study. The Canadian journal of cardiology Moayedi, Y., Hershman, S. G., Henricksen, E. J., Lee, R., Han, J., Bougouin, W., Khush, K. K., Ross, H. J., Teuteberg, J. J. 2020

    View details for DOI 10.1016/j.cjca.2020.07.005

    View details for PubMedID 32681856

  • Molecular Transducers of Physical Activity Consortium (MoTrPAC): Mapping the Dynamic Responses to Exercise. Cell Sanford, J. A., Nogiec, C. D., Lindholm, M. E., Adkins, J. N., Amar, D., Dasari, S., Drugan, J. K., Fernandez, F. M., Radom-Aizik, S., Schenk, S., Snyder, M. P., Tracy, R. P., Vanderboom, P., Trappe, S., Walsh, M. J., Molecular Transducers of Physical Activity Consortium, Adkins, J. N., Amar, D., Dasari, S., Drugan, J. K., Evans, C. R., Fernandez, F. M., Li, Y., Lindholm, M. E., Nogiec, C. D., Radom-Aizik, S., Sanford, J. A., Schenk, S., Snyder, M. P., Tomlinson, L., Tracy, R. P., Trappe, S., Vanderboom, P., Walsh, M. J., Alekel, D. L., Bekirov, I., Boyce, A. T., Boyington, J., Fleg, J. L., Joseph, L. J., Laughlin, M. R., Maruvada, P., Morris, S. A., McGowan, J. A., Nierras, C., Pai, V., Peterson, C., Ramos, E., Roary, M. C., Williams, J. P., Xia, A., Cornell, E., Rooney, J., Miller, M. E., Ambrosius, W. T., Rushing, S., Stowe, C. L., Rejeski, W. J., Nicklas, B. J., Pahor, M., Lu, C., Trappe, T., Chambers, T., Raue, U., Lester, B., Bergman, B. C., Bessesen, D. H., Jankowski, C. M., Kohrt, W. M., Melanson, E. L., Moreau, K. L., Schauer, I. E., Schwartz, R. S., Kraus, W. E., Slentz, C. A., Huffman, K. M., Johnson, J. L., Willis, L. H., Kelly, L., Houmard, J. A., Dubis, G., Broskey, N., Goodpaster, B. H., Sparks, L. M., Coen, P. M., Cooper, D. M., Haddad, F., Rankinen, T., Ravussin, E., Johannsen, N., Harris, M., Jakicic, J. M., Newman, A. B., Forman, D. D., Kershaw, E., Rogers, R. J., Nindl, B. C., Page, L. C., Stefanovic-Racic, M., Barr, S. L., Rasmussen, B. B., Moro, T., Paddon-Jones, D., Volpi, E., Spratt, H., Musi, N., Espinoza, S., Patel, D., Serra, M., Gelfond, J., Burns, A., Bamman, M. M., Buford, T. W., Cutter, G. R., Bodine, S. C., Esser, K., Farrar, R. P., Goodyear, L. J., Hirshman, M. F., Albertson, B. G., Qian, W., Piehowski, P., Gritsenko, M. A., Monore, M. E., Petyuk, V. A., McDermott, J. E., Hansen, J. N., Hutchison, C., Moore, S., Gaul, D. A., Clish, C. B., Avila-Pacheco, J., Dennis, C., Kellis, M., Carr, S., Jean-Beltran, P. M., Keshishian, H., Mani, D. R., Clauser, K., Krug, K., Mundorff, C., Pearce, C., Ivanova, A. A., Ortlund, E. A., Maner-Smith, K., Uppal, K., Zhang, T., Sealfon, S. C., Zavlasky, E., Nair, V., Li, S., Jain, N., Ge, Y., Sun, Y., Nudelman, G., Ruf-Zamojski, F., Smith, G., Pincas, N., Rubenstein, A., Amper, M. A., Seenarine, N., Lappalainen, T., Lanza, I. R., Nair, K. S., Klaus, K., Montgomery, S. B., Smith, K. S., Gay, N. R., Zhao, B., Hung, C. J., Zebarjadi, N., Balliu, B., Fresard, L., Burant, C. F., Li, J. Z., Kachman, M., Soni, T., Raskind, A. B., Gerszten, R., Robbins, J., Ilkayeva, O., Muehlbauer, M. J., Newgard, C. B., Ashley, E. A., Wheeler, M. T., Jimenez-Morales, D., Raja, A., Dalton, K. P., Zhen, J., Kim, Y. S., Christle, J. W., Marwaha, S., Chin, E. T., Hershman, S. G., Hastie, T., Tibshirani, R., Rivas, M. A. 2020; 181 (7): 1464–74


    Exercise provides a robust physiological stimulus that evokes cross-talk among multiple tissues that when repeated regularly (i.e., training) improves physiological capacity, benefits numerous organ systems, and decreases the risk for premature mortality. However, a gap remains in identifying the detailed molecular signals induced by exercise that benefits health and prevents disease. The Molecular Transducers of Physical Activity Consortium (MoTrPAC) was established to address this gap and generate a molecular map of exercise. Preclinical and clinical studies will examine the systemic effects of endurance and resistance exercise across a range of ages and fitness levels by molecular probing of multiple tissues before and after acute and chronic exercise. From this multi-omic and bioinformatic analysis, a molecular map of exercise will be established. Altogether, MoTrPAC will provide a public database that is expected to enhance our understanding of the health benefits of exercise and to provide insight into how physical activity mitigates disease.

    View details for DOI 10.1016/j.cell.2020.06.004

    View details for PubMedID 32589957

  • Mobile Health Monitoring of Cardiac Status ANNUAL REVIEW OF BIOMEDICAL DATA SCIENCE, VOL 3, 2020 Christle, J. W., Hershman, S. G., Soto, J., Ashley, E. A., Altman, R. B. 2020; 3: 243–63
  • The effect of digital physical activity interventions on daily step count: a randomised controlled crossover substudy of the MyHeart Counts Cardiovascular Health Study. The Lancet. Digital health Shcherbina, A., Hershman, S. G., Lazzeroni, L., King, A. C., O'Sullivan, J. W., Hekler, E., Moayedi, Y., Pavlovic, A., Waggott, D., Sharma, A., Yeung, A., Christle, J. W., Wheeler, M. T., McConnell, M. V., Harrington, R. A., Ashley, E. A. 2019; 1 (7): e344-e352


    Smartphone apps might enable interventions to increase physical activity, but few randomised trials testing this hypothesis have been done. The MyHeart Counts Cardiovascular Health Study is a longitudinal smartphone-based study with the aim of elucidating the determinants of cardiovascular health. We aimed to investigate the effect of four different physical activity coaching interventions on daily step count in a substudy of the MyHeart Counts Study.In this randomised, controlled crossover trial, we recruited adults (aged ≥18 years) in the USA with access to an iPhone smartphone (Apple, Cupertino, CA, USA; version 5S or newer) who had downloaded the MyHeart Counts app (version 2.0). After completion of a 1 week baseline period of interaction with the MyHeart Counts app, participants were randomly assigned to receive one of 24 permutations (four combinations of four 7 day interventions) in a crossover design using a random number generator built into the app. Interventions consisted of either daily prompts to complete 10 000 steps, hourly prompts to stand following 1 h of sitting, instructions to read the guidelines from the American Heart Association website, or e-coaching based upon the individual's personal activity patterns from the baseline week of data collection. Participants completed the trial in a free-living setting. Due to the nature of the interventions, participants could not be masked from the intervention. Investigators were not masked to intervention allocation. The primary outcome was change in mean daily step count from baseline for each of the four interventions, assessed in the modified intention-to-treat analysis set, which included all participants who had completed 7 days of baseline monitoring and at least 1 day of one of the four interventions. This trial is registered with, NCT03090321.Between Dec 12, 2016, and June 6, 2018, 2783 participants consented to enrol in the coaching study, of whom 1075 completed 7 days of baseline monitoring and at least 1 day of one of the four interventions and thus were included in the modified intention-to-treat analysis set. 493 individuals completed the full set of assigned interventions. All four interventions significantly increased mean daily step count from baseline (mean daily step count 2914 [SE 74]): mean step count increased by 319 steps (75) for participants in the American Heart Association website prompt group (p<0·0001), 267 steps (74) for participants in the hourly stand prompt group (p=0·0003), 254 steps (74) for participants in the cluster-specific prompts group (p=0·0006), and by 226 steps (75) for participants in the 10 000 daily step prompt group (p=0·0026 vs baseline).Four smartphone-based physical activity coaching interventions significantly increased daily physical activity. These findings suggests that digital interventions delivered via a mobile app have the ability to increase short-term physical activity levels in a free-living cohort.Stanford Data Science Initiative.

    View details for DOI 10.1016/S2589-7500(19)30129-3

    View details for PubMedID 33323209

  • Physical activity, sleep and cardiovascular health data for 50,000 individuals from the MyHeart Counts Study. Scientific data Hershman, S. G., Bot, B. M., Shcherbina, A., Doerr, M., Moayedi, Y., Pavlovic, A., Waggott, D., Cho, M. K., Rosenberger, M. E., Haskell, W. L., Myers, J., Champagne, M. A., Mignot, E., Salvi, D., Landray, M., Tarassenko, L., Harrington, R. A., Yeung, A. C., McConnell, M. V., Ashley, E. A. 2019; 6 (1): 24


    Studies have established the importance of physical activity and fitness for long-term cardiovascular health, yet limited data exist on the association between objective, real-world large-scale physical activity patterns, fitness, sleep, and cardiovascular health primarily due to difficulties in collecting such datasets. We present data from the MyHeart Counts Cardiovascular Health Study, wherein participants contributed data via an iPhone application built using Apple's ResearchKit framework and consented to make this data available freely for further research applications. In this smartphone-based study of cardiovascular health, participants recorded daily physical activity, completed health questionnaires, and performed a 6-minute walk fitness test. Data from English-speaking participants aged 18 years or older with a US-registered iPhone who agreed to share their data broadly and who enrolled between the study's launch and the time of the data freeze for this data release (March 10 2015-October 28 2015) are now available for further research. It is anticipated that releasing this large-scale collection of real-world physical activity, fitness, sleep, and cardiovascular health data will enable the research community to work collaboratively towards improving our understanding of the relationship between cardiovascular indicators, lifestyle, and overall health, as well as inform mobile health research best practices.

    View details for PubMedID 30975992

  • Physical activity, sleep and cardiovascular health data for 50,000 individuals from the MyHeart Counts Study SCIENTIFIC DATA Hershman, S. G., Bot, B. M., Shcherbina, A., Doerr, M., Moayedi, Y., Pavlovic, A., Waggott, D., Cho, M. K., Rosenberger, M. E., Haskell, W. L., Myers, J., Champagne, M., Mignot, E., Salvi, D., Landray, M., Tarassenko, L., Harrington, R. A., Yeung, A. C., McConnell, M. V., Ashley, E. A. 2019; 6
  • Perceived Generational, Geographic, and Sex-Based Differences in Choosing a Career in Advanced Heart Failure. Circulation. Heart failure Moayedi, Y. n., Hershman, S. G., Ross, H. J., Khush, K. K., Teuteberg, J. J. 2019; 12 (7): e005754
  • The Myheart Counts Cardiovascular Health Study: A Randomized Controlled Trial of Digital Health Coaching for Physical Activity Promotion Shcherbina, A., Hershman, S., King, A. C., Hekler, E., Pavlovic, A., Waggott, D., McConnell, M. V., Ashley, E. A., Harrington, R. A. LIPPINCOTT WILLIAMS & WILKINS. 2018: E767
  • Data Descriptor: The asthma mobile health study, smartphone data collected using ResearchKit SCIENTIFIC DATA Chan, Y., Bot, B. M., Zweig, M., Tignor, N., Ma, W., Suver, C., Cedeno, R., Scott, E. R., Hershman, S., Schadt, E. E., Wang, P. 2018; 5: 180096


    Widespread adoption of smart mobile platforms coupled with a growing ecosystem of sensors including passive location tracking and the ability to leverage external data sources create an opportunity to generate an unprecedented depth of data on individuals. Mobile health technologies could be utilized for chronic disease management as well as research to advance our understanding of common diseases, such as asthma. We conducted a prospective observational asthma study to assess the feasibility of this type of approach, clinical characteristics of cohorts recruited via a mobile platform, the validity of data collected, user retention patterns, and user data sharing preferences. We describe data and descriptive statistics from the Asthma Mobile Health Study, whereby participants engaged with an iPhone application built using Apple's ResearchKit framework. Data from 6346 U.S. participants, who agreed to share their data broadly, have been made available for further research. These resources have the potential to enable the research community to work collaboratively towards improving our understanding of asthma as well as mobile health research best practices.

    View details for PubMedID 29786695

  • Big data, artificial intelligence, and cardiovascular precision medicine EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT Krittanawong, C., Johnson, K. W., Hershman, S. G., Tang, W. 2018; 3 (5): 305–17